{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import joblib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_KGB = pd.read_csv('./score_KGB.csv')\n",
    "score_AR = pd.read_csv('./score_AR.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8438206358>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(score_KGB.prob)\n",
    "sns.distplot(score_AR.score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33466</td>\n",
       "      <td>0.132815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33467</td>\n",
       "      <td>0.248722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33468</td>\n",
       "      <td>0.037473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33469</td>\n",
       "      <td>0.060895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33470</td>\n",
       "      <td>0.175726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33471</td>\n",
       "      <td>0.093106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33472</td>\n",
       "      <td>0.370039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33473</td>\n",
       "      <td>0.062400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33474</td>\n",
       "      <td>0.166040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33475</td>\n",
       "      <td>0.016872</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id      prob\n",
       "0  33466  0.132815\n",
       "1  33467  0.248722\n",
       "2  33468  0.037473\n",
       "3  33469  0.060895\n",
       "4  33470  0.175726\n",
       "5  33471  0.093106\n",
       "6  33472  0.370039\n",
       "7  33473  0.062400\n",
       "8  33474  0.166040\n",
       "9  33475  0.016872"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_KGB.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_com = score_KGB.prob.rank()+score_AR.score.rank()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['score_com']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(score_com,'score_com')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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